---
title: "Rapport pays Norvège"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```

Column
-------------------------------------

### Norway map

```{r, include=FALSE}
library(sf)
library(ggplot2)
library(tmap)
library(tmaptools)
library(leaflet)
library(dplyr)

mydata <- readr::read_csv("Density Map/Density per Fylker.csv")

mymap <- st_read("Density Map/NORWAY SHAPE/NOR_adm1.shp", stringsAsFactors = FALSE)

datamap1 <- inner_join(mymap, mydata)

datamap2 <- ifelse(datamap1$Density >= 0 & datamap1$Density <= 5, "0-5", ifelse(datamap1$Density >= 6 & datamap1$Density <= 10, "6-10", ifelse(datamap1$Density >= 11 & datamap1$Density <= 20, "11-20", ifelse(datamap1$Density >= 21 & datamap1$Density <= 50, "21-50", ifelse(datamap1$Density >= 51 & datamap1$Density <= 100, "51-100", ifelse(datamap1$Density >= 101 & datamap1$Density <= 200, "101-200", ifelse(datamap1$Density >= 201 & datamap1$Density <= 500, "201-500", ifelse(datamap1$Density >= 501, "501+", NA))))))))

cut(datamap1$Density, breaks=c(0, 5, 10, 20, 50, 100, 200, 500, Inf), labels=c("0-5", "6-10", "11-20", "21-50", "51-100", "101-200","201-500", "500+"))

mylabels <- c("0-5", "6-10", "11-20", "21-50", "51-100", "101-200", "201-500", "500+")

```

```{r, include=FALSE}

datamap1$Density <- base::cut(datamap1$Density,
                        breaks = c(0, 5, 10, 20, 50, 100, 200, 500, 2000), 
                        labels = mylabels, right = F, ordered_result = T)

```

```{r, include=FALSE}

tm_shape(datamap1) +
  tm_polygons("Density", id = "NAME_1", palette = "Oranges")+ "Density"
  theme_minimal()
  
tmap_mode("view")
DensityMap <- tmap_last()

```

```{r}

DensityMap

#tmap_save(DensityMap, "DensityMap.html")

```

### Description

This map highlights the great disparity that exists on the Norwegian territory in terms of density. Indeed, one notices immediately that the vast majority of the country is very sparsely populated. Most regions have a density of less than 20 inhabitants per km². By way of comparison, the average density in France is about 117 inhabitants per km² compared to 13 in Norway. 

Column
-------------------------------------

### Air pollution

```{r, include=FALSE}

library(spiR)

EQU <- sqs_spi_data(country = c("AUS", "DNK", "NOR", "NLD", "DEU"), 
                       year = c("2014","2015","2016", "2017", "2018", "2019"), 
                       indicators = "EQU_2")

```

```{r, include=FALSE}

library(ggplot2)
library(plotly)

EQU <- EQU[order(EQU$countryName),]

graph6 <- ggplot(EQU, aes(var_year, value, color = countryName)) +
  geom_point(aes(size = 1, frame = var_year)) +
  scale_x_log10() + theme(plot.subtitle = element_text(vjust = 1), 
    plot.caption = element_text(vjust = 1), 
    axis.text.y = element_text(size = 5)) +
    theme(axis.text.y = element_text(size = 7)) + labs(title = "Air pollution attributable deaths") +labs(x = "Year", y = "Deaths per year") + labs(colour = NULL, size = NULL)

graph7 <- ggplotly(graph6)
graph7

```

```{r}
graph7
```

Column {.tabset data-width=700}
-------------------------------------

### Health and Wellness 

```{r, include=FALSE}

library(spiR)

HWE <- sqs_spi_data(country = c("AUS", "DNK", "NOR", "NLD", "DEU"), 
                       year = c("2014","2015","2016", "2017", "2018", "2019"), 
                       indicators = "HWE")

```

```{r, include=FALSE}
library(ggplot2)
library(plotly)

graph2 <- ggplot(HWE) +
 aes(x = var_year, y = value, colour = countryName, group = countryName) +
 geom_line(size = 1.2) +
 scale_color_hue() +
 labs(x = "Year", y = "Health & Wellness Indicator", title = "Health & Wellness", color = "Countries") +
 theme_minimal() 

graph2 <- ggplotly(graph2)
graph2

```

```{r}
graph2
```


### Wellbeing

```{r, include=FALSE}
library(spiR)

FOW <- sqs_spi_data(country = c("AUS", "DNK", "NOR", "NLD", "DEU"), 
                       year = c("2014","2015","2016", "2017", "2018", "2019"), 
                       indicators = "FOW")
```

```{r, include=FALSE}

library(ggplot2)
library(plotly)

graph <- ggplot(FOW) +
 aes(x = var_year, y = value, colour = countryName, group = countryName) +
 geom_line(size = 1.2) +
 scale_color_hue() +
 labs(x = "Year", y = "Wellbeing Indicator", title = "Wellbeing", color = "Countries") +
 theme_minimal() 

graph1 <- ggplotly(graph)
graph1

```

```{r}
graph1
```

### Electricty Quality 

```{r, include=FALSE}

library(spiR)

SHE_2 <- sqs_spi_data(country = c("AUS", "DNK", "NOR", "NLD", "DEU"), 
                       year = c("2014","2015","2016", "2017", "2018", "2019"), 
                       indicators = "SHE_2")


SHE_2

```

```{r, include=FALSE}
library(ggplot2)
library(plotly)

graph3 <- ggplot(SHE_2) +
 aes(x = var_year, y = value, colour = countryName, group = countryName) +
 geom_line(size = 1.2) +
 scale_color_hue() +
 labs(x = "Year", y = "Electricity Quality Indicator", title = "Electricity Quality", color = "Countries") +
 theme_minimal() 

graph31 <- ggplotly(graph3)
graph31

```

```{r}
graph31
```